Phase‐coherency filtering of reflection seismic data

Geophysics ◽  
1985 ◽  
Vol 50 (9) ◽  
pp. 1505-1509 ◽  
Author(s):  
John D. McGlynn ◽  
George E. Ioup
Keyword(s):  
Seismic Data ◽  
Output Energy ◽  
Signal To Noise ◽  
Reflection Seismic ◽  
Advantages And Disadvantages ◽  
Common Depth Point ◽  
Multichannel Data ◽  
Band Pass ◽  
Standard Techniques

In addition to the standard techniques of stacking and digital band‐pass filtering for enhancing signal‐to‐noise (S/N) ratio for common‐depth‐point (CDP) reflection seismic data, a number of alternative or supplementary methods are available: (1) weighted mixing of adjacent traces, (2) semblance weighting of multichannel data based upon interchannel semblances (Neidell and Taner, 1971), and (3) filtering with the output energy filter (Robinson and Treitel, 1980). Each approach has advantages and disadvantages.

STATISTICALLY OPTIMAL STACKING OF SEISMIC DATA

Geophysics ◽  
1970 ◽  
Vol 35 (3) ◽  
pp. 436-446 ◽  
Author(s):  
John C. Robinson
Keyword(s):  
Seismic Data ◽  
Field Data ◽  
Mean Value ◽  
Seismic Model ◽  
Seismic Record ◽  
Signal To Noise ◽  
Statistical Procedures ◽  
Common Depth Point ◽  
Noise Energy ◽  
Seismic Records

A theory for weighting seismic records in the stacking process has been developed from a statistical seismic model. The model applies to common‐depth‐point seismic records which have been statically and dynamically corrected; the same model applies to an ordinary stacking procedure. The model stipulates for the signal and noise components, respectively, of a seismic record that (1) the signal is coincident with and similarly shaped to the signal on other records, and (2) the noise is statistically independent of that on any other record and of the signal and has zero mean value. In accord with the model, a seismic record is completely described for the purpose of weighting by its signal scale and its signal‐to‐noise energy ratio. Several statistical procedures for evaluating these parameters for seismic field data are presented. The most favorable procedure is demonstrated with both synthetic and field seismic records.

Prestack structurally constrained impedance inversion

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. R89-R103 ◽  
Author(s):  
Haitham Hamid ◽  
Adam Pidlisecky ◽  
Larry Lines
Keyword(s):  
Seismic Data ◽  
Local Structure ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Simultaneous Inversion ◽  
Common Depth Point ◽  
Inversion Methods ◽  
Impedance Inversion ◽  
Regularization Operator ◽  
Complex Geology

Classical prestack impedance inversion methods are based on performing a common-depth point (CDP) by CDP inversion using Tikhonov-type regularization. We refer to it as lateral unconstrained inversion (1D-LUI). Prestack seismic data usually have a low signal-to-noise ratio, and the 1D-LUI approach is sensitive to noise. The inversion results can be noisy and lead to an unfocused transition between vertical formation boundaries. The lateral constrained inversion (1D-LCI) can suppress the noise and provide sharp boundaries between inverted 1D models in regions where the layer dips are less than 20°. However, in complex geology, the disadvantage of using the 1D-LC approach is the lateral smearing of the steeply dipping layers. We have developed a structurally constrained inversion (1D-SCI) approach to mitigate the smearing associated with 1D-LCI. SCI involves simultaneous inversion of all seismic CDPs using a regularization operator that forces the solution to honor the local structure. The results of the 1D-SCI were superior compared with the 1D-LUI and 1D-LCI approaches. The steeply dipping layers are clearly visible on the SCI inverted results.

THE DETECTION AND DEVELOPMENT OF SILURIAN REEFS IN NORTHERN MICHIGAN

Geophysics ◽  
1976 ◽  
Vol 41 (4) ◽  
pp. 646-658 ◽  
Author(s):  
W. G. Caughlin ◽  
F. J. Lucia ◽  
N. L. McIver
Keyword(s):  
Seismic Data ◽  
Seismic Event ◽  
Gas Production ◽  
Noise Levels ◽  
Static Correction ◽  
Reflection Seismic ◽  
Common Depth Point ◽  
Carbonate Buildups ◽  
Shelf Margin ◽  
Northern Michigan

The reflection seismic method has proven to be an excellent technique for locating and mapping Silurian reef buildups in northern Michigan. Current industry estimates of total discovery volumes are 400 to 600 million barrels of oil and 3 to 5 trillion cu ft of gas. Production is from carbonate buildups (reef pinnacles) whose areal extents range from 40 to 600 acres at depths of 3000 to 7000 ft. Extending northeast‐southwest across the northern part of Michigan, the reef trend is 10 to 20 miles wide. Reef heights increase basinward from the shelf margin, growing to a maximum of 600+ ft. Reef porosity throughout the trend is variable, ranging from 5 to 30 percent, depending upon a combination of diagenetic processes. Reefs grew on a ramp from the Niagaran shelf edge to a flat basinal plain. A series of salt and carbonate beds were deposited around and over these carbonate buildups. Anomalies are recognizable from seismic data because the inter‐reef section produces a strong characteristic seismic event which becomes weak and disrupted in the presence of a buildup. The Niagaran shelf margin can be mapped because it produces a seismic response similar to that of a pinnacle reef. In some areas, seismic interpretation is difficult due to high noise levels and distortion of seismic data by the irregular distribution of Pleistocene glacial deposits. Data quality has been improved through a combination of field techniques, common‐depth‐point stacking, static correction refinement, and filtering.

Sign‐bit amplitude recovery with applications to seismic data

Geophysics ◽  
1982 ◽  
Vol 47 (11) ◽  
pp. 1527-1539 ◽  
Author(s):  
J. T. O’Brien ◽  
W. P. Kamp ◽  
G. M. Hoover
Keyword(s):  
Seismic Data ◽  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Recovery Process ◽  
Signal To Noise ◽  
Common Depth Point ◽  
Seismic Recording ◽  
Noise Ratio ◽  
Correct Application ◽  
Complete Amplitude

Sign‐bit digital recording means that only the sign of the analog signal is recorded with one bit. In conventional seismic recording, 16 to 20 binary bits are acquired per sample point. The economic advantages of sign‐bit acquisition are immediately obvious. Complete amplitude recovery, comparable to full‐gain recording, can be achieved by correct application of sign‐bit techniques. We describe the amplitude recovery process in a semiintuitive manner to promote the understanding necessary for proper application of the technique. The dynamic range requirements in seismic applications are discussed. Sign‐bit digitization is a completely viable technique for recording seismic data, provided that two conditions are fulfilled. First, in real time, the coherent‐signal‐to‐randomnoise‐ratio must be ⩽1.0. Second, the data must be recorded with sufficient redundancy. Redundancy is achieved by source repetition, sweep correlation, and high‐fold common‐depth‐point stacking, usually in combination. Failure to abide by these two restrictions results in (1) incomplete amplitude recovery, i.e., clipped data, and (2) insufficient dynamic range in the recovered signal. We derive the requirement that the signal‐to‐noise ratio be less than one; we also discuss the consequences of violating that requirement, namely clipping, at various points in the processing sequence. The amount of information lost is proportional to the degree of clipping; a small amount can be tolerated. Calculated expectation values show that (subject to the requirement that the signal‐to‐noise ratio be less than 1.0) an unbiased estimator can be chosen. The variance of these estimators is approximately the same as that for full‐gain seismic techniques. With sufficient redundancy, the variance can be made as small as necessary to achieve the required dynamic range. With proper attention to these findings, sign‐bit digitized data are found to be a totally viable tool.

MicroRNA annotation in plants: current status and challenges

2021 ◽  
Author(s):  
Yongxin Zhao ◽  
Zheng Kuang ◽  
Ying Wang ◽  
Lei Li ◽  
Xiaozeng Yang
Keyword(s):  
Small Rna ◽  
Signal To Noise Ratio ◽  
Current Status ◽  
Mirna Biogenesis ◽  
Plant Mirnas ◽  
Signal To Noise ◽  
Advantages And Disadvantages ◽  
Plant Mirna ◽  
History Of ◽  
Mirna Biogenesis Pathway

Abstract Last two decades, the studies on microRNAs (miRNAs) and the numbers of annotated miRNAs in plants and animals have surged. Herein, we reviewed the current progress and challenges of miRNA annotation in plants. Via the comparison of plant and animal miRNAs, we pinpointed out the difficulties on plant miRNA annotation and proposed potential solutions. In terms of recalling the history of methods and criteria in plant miRNA annotation, we detailed how the major progresses made and evolved. By collecting and categorizing bioinformatics tools for plant miRNA annotation, we surveyed their advantages and disadvantages, especially for ones with the principle of mimicking the miRNA biogenesis pathway by parsing deeply sequenced small RNA (sRNA) libraries. In addition, we summarized all available databases hosting plant miRNAs, and posted the potential optimization solutions such as how to increase the signal-to-noise ratio (SNR) in these databases. Finally, we discussed the challenges and perspectives of plant miRNA annotations, and indicated the possibilities offered by an all-in-one tool and platform according to the integration of artificial intelligence.

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